def compare_state_outcomes(genome, sensor_input_file):
# This function evaluates the difference of the states by running it on a randomly generated set
# input values and reporting the distance between the output vectors of the states.
# Load the randomly generated sensor inputs
with open(sensor_input_file, 'r') as csv_file:
csv_reader = csv.reader(csv_file, quoting=csv.QUOTE_NONNUMERIC)
sensor_vectors = [row for row in csv_reader]
# Evaluate the sensor inputs on each genome.
state_differences = dict()
activations = ActivationFunctionSet()
aggregations = AggregationFunctionSet()
for enc_state1 in itervalues(genome.states):
state1 = State(1, enc_state1.weights, enc_state1.biases,
aggregations.get(enc_state1.aggregation),
activations.get(enc_state1.activation))
for enc_state2 in itervalues(genome.states):
key_pair = (enc_state1.key, enc_state2.key)
if key_pair[0] != key_pair[1] and key_pair not in state_differences \
and key_pair[::-1] not in state_differences:
state2 = State(2, enc_state2.weights, enc_state2.biases,
aggregations.get(enc_state2.aggregation),
activations.get(enc_state2.activation))
output_pairs = [(state1.activate(inputs),
state2.activate(inputs))
for inputs in sensor_vectors]
distances = [
np.average(
np.abs(
np.array(output_pair[0]) -
np.array(output_pair[1])))
for output_pair in output_pairs
]
state_differences[key_pair] = np.average(distances), np.std(
distances)
return state_differences
def test_1_in_1_out(self):
state = State(42)
state.set_biases([1])
state.set_weights([[2]])
self.assertListEqual(state.activate([5]), [11])
def test_2_in_3_out(self):
state = State(42)
state.set_biases([0, 1, 2])
state.set_weights([[2, 3], [4, 5], [1, 2]])
self.assertListEqual(state.activate([5, 10]), [40, 71, 27])
def test_3_in_2_out(self):
state = State(42)
state.set_biases([0, 1])
state.set_weights([[2, 3, 1], [4, 5, 1]])
self.assertListEqual(state.activate([5, 10, 20]), [60, 91])
def test_2_in_1_out(self):
state = State(42)
state.set_biases([0])
state.set_weights([[2, 3]])
self.assertListEqual(state.activate([5, 10]), [40])
